__author__ = 'Benjamin Weaver <benjamin.weaver@noirlab.edu>'
__version__ = '202100302'
__datasets__ = ['sdss_dr16']
__keywords__ = ['HowTo','spectra','query','visualization','SDSS']

How to Plot Spectral Data with Prospect, Specutils and the Astro Data Lab Spectrum Service

Table of Contents

• Goals
• Summary
• Disclaimer & Attribution
• Imports & Setup
• Authentication
• SDSS/eBOSS spPlate file
  • Find Plate 2955 in the Spectrum Service
  • Spectrum Identifiers
  • Spectrum Service: Flux versus Wavelength
  • Redshift and other Metadata
    • Redshift Catalog
    • Target Catalog
• Launch the Visualization
  • Interface Visualization Details
• SDSS Spectra and Redshifts from Files
• References & Resources

Goals

Obtain SDSS spectra using the Astro Data Lab spectrum service, convert data to specutils objects as needed, and use Prospect to display the data.

Summary

Prospect is an interactive spectrum visualization service that is being actively used by the DESI project for visual inspection of commissioning and survey validation spectra right now. Rather than just a simple flux versus wavelength plot, the interactive spectrum display provides pan, zoom, known spectral lines, targeting and other catalog information, metadata, etc. The Bokeh library handles the low-level interactivity.

We can leverage the generic spectrum container objects provided by specutils to allow Prospect to plot data from other surveys. In this example, we are using SDSS DR16.

In the future we will extend this functionality to additional instruments. For example, we are excited by the possibility of using Prospect and the Astro Data Lab spectrum service for Gemini spectra. And, of course, once DESI data become public, both the spectrum service and this tool will be ready.

Disclaimer & Attribution

If you use this notebook for your published science, please acknowledge the following:

• Astro Data Lab concept paper: Fitzpatrick et al., "The NOAO Data Laboratory: a conceptual overview", SPIE, 9149, 2014, http://dx.doi.org/10.1117/12.2057445
• Astro Data Lab disclaimer: https://datalab.noirlab.edu/disclaimers.php

Imports & Setup

This cell performs all necessary imports for this notebook.

import os
import sys
import time
from getpass import getpass
#
# Numpy, astropy, etc.
#
import numpy as np
import astropy.units as u
from astropy.table import Table
from astropy.nddata import InverseVariance
from specutils import Spectrum1D, SpectrumCollection, SpectrumList
#
# Prospect imports
#
from prospect.specutils import read_spPlate, read_spZbest
from prospect.viewer import plotspectra
#
# Astro Data Lab imports
#
from dl import specClient as spec  # primary spectral data client interface
from dl import storeClient as sc   # needed to use virtual storage
from dl import queryClient as qc   # needed to query Astro Data Lab catalogs
from dl import authClient as ac    # needed for login authentication

start_time = time.time()  # save start time of notebook run

Authentication

Much of the functionality of spectrum services can be accessed without explicitly logging into Astro Data Lab (the service then uses an anonymous login). But some capabilities, for instance saving the results of your queries to your virtual storage space, require a login (i.e. you will need a registered user account).

If you need to log in to Astro Data Lab, uncomment the ac.login() command and respond according to the prompts. If you have previously logged into Astro Data Lab, this cell will simply print your active user name.

# ac.login(input("Enter user name: (+ENTER) "),getpass("Enter password: (+ENTER) "))
ac.whoAmI()

'demo01'

SDSS/eBOSS spPlate file

SDSS spectra are stored per-plate in spPlate files. These contain 640 spectra for the original SDSS spectrograph or 1000 spectra for the BOSS/eBOSS spectrograph. All spectra in an individual spPlate file have a common wavelength solution, so a single spPlate file can be represented by a Spectrum1D object. The Prospect visualization package can then take this Spectrum1D object as input data.

Find Plate 2955 in the Spectrum Service

In the cells below, we will find spectra corresponding to SDSS plate 2955, but using the spectrum service instead of reading the spectra from a file. This starts by finding the coordinates of the plate center.

plate = 2955
q = f"SELECT ra, dec FROM sdss_dr16.platex WHERE plate = {plate}"
r = qc.query(sql=q, fmt='array')
r

array([235.4204  ,   1.644156])

Spectrum Identifiers

SDSS plates are circular, so we'll do a cone search around the plate center found above. This gets us the spectrum identifiers we need to retrieve the spectra themselves.

sdss_ids = spec.query(r[0], r[1], 1.5, fmt='array', out='', constraint=f'plate = {plate} ORDER BY specobjid LIMIT 50')
sdss_ids

array([3327035125891360768, 3327036500280895488, 3327037050036709376,
       3327038424426244096, 3327038974182057984, 3327040073693685760,
       3327040348571592704, 3327040623449499648, 3327040898327406592,
       3327041173205313536, 3327041722961127424, 3327041997839034368,
       3327042822472755200, 3327043097350662144, 3327043372228569088,
       3327043921984382976, 3327044196862289920, 3327045021496010752,
       3327045296373917696, 3327046121007638528, 3327046670763452416,
       3327047495397173248, 3327048045152987136, 3327048320030894080,
       3327048594908801024, 3327048869786707968, 3327049144664614912,
       3327049419542521856, 3327049694420428800, 3327050244176242688,
       3327051068809963520, 3327051343687870464, 3327052443199498240,
       3327052992955312128, 3327053267833219072, 3327053817589032960,
       3327054092466939904, 3327054642222753792, 3327054917100660736,
       3327055191978567680, 3327055466856474624, 3327056016612288512,
       3327056566368102400, 3327056841246009344, 3327057116123916288,
       3327057391001823232, 3327057665879730176, 3327057940757637120,
       3327058215635544064, 3327058490513451008])

Spectrum Service: Flux versus Wavelength

Now we fetch the spectra. The option align=True directes the service to return all spectra as a single Spectrum1D object with a common wavelength axis for all spectra. Since all spectra on a single SDSS plate have a common wavelength axis anyway, this is a simple operation.

%%time
sdss = spec.getSpec(sdss_ids, fmt='Spectrum1D', align=True)
sdss

CPU times: user 27.5 ms, sys: 22.2 ms, total: 49.7 ms
Wall time: 225 ms

<Spectrum1D(flux=<Quantity [[0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           ...,
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.],
           [0., 0., 0., ..., 0., 0., 0.]] erg / (Angstrom cm2 s)>, spectral_axis=<SpectralAxis [3802.77  , 3803.6448, 3804.522 , ..., 9217.223 , 9219.344 , 9221.464 ] Angstrom>, uncertainty=InverseVariance([[0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.],
                 ...,
                 [0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.]]))>

Redshift and other Metadata

The Prospect viewer requires certain spectral metadata that are normally provided with SDSS spectra--i.e., these metadata are stored alongside the flux versus wavelength data. There are two categories of metadata:

1. Redshift catalog: this contains the redshift (how far away it is) and classification (what type of object it is) for each spectrum, as identified by the SDSS pipeline.
2. Target catalog: this contains the information needed for taking a spectrum of the object in the first place. This includes information about the object as identified in SDSS imaging (RA, Dec, optical magnitude), as well as the rationale for taking a spectrum of the object.

In the cells below, we will extract the necessary metadata from the specobj and photoplate tables and reformat them slightly to match the appearance of these data as they would be extracted from SDSS files.

Redshift Catalog

This query extracts the redshift and classification information from the specobj table.

q = "SELECT specobjid, class, subclass, z, zerr, rchi2diff, zwarning FROM sdss_dr16.specobj WHERE specobjid IN ({0}) ORDER BY specobjid".format(','.join(map(str, sdss_ids.tolist())))
sdss_z = qc.query(sql=q, fmt='table')
for c in ('specobjid', 'class', 'subclass', 'z', 'zerr', 'rchi2diff', 'zwarning'):
    if c == 'zerr':
        sdss_z.rename_column(c, 'Z_ERR')
    else:
        sdss_z.rename_column(c, c.upper())
sdss_z

Table length=50

SPECOBJID	CLASS	SUBCLASS	Z	Z_ERR	RCHI2DIFF	ZWARNING
int64	str6	str11	float64	float64	float64	int64
3327035125891360768	GALAXY	--	0.26219922	6.426468e-05	0.16153336	0
3327036500280895488	GALAXY	--	0.21097003	5.7919853e-05	0.13921309	0
3327037050036709376	STAR	K5	-2.3494376e-06	1.4350197e-05	0.49699175	0
3327038424426244096	GALAXY	--	0.2605426	6.229882e-05	0.22558784	0
3327038974182057984	GALAXY	--	0.11276812	1.5994665e-05	0.510905	0
3327040073693685760	GALAXY	--	0.06364931	2.8312488e-05	0.42436635	0
3327040348571592704	GALAXY	--	0.20836118	4.189276e-05	1.1794078	0
3327040623449499648	GALAXY	--	0.0	0.0	0.0	134
3327040898327406592	GALAXY	--	0.20784536	4.1236628e-05	0.61972916	0
3327041173205313536	QSO	--	0.04825218	0.00014616082	0.00018835068	4
...	...	...	...	...	...	...
3327055466856474624	GALAXY	--	0.16715993	3.366827e-05	0.5121331	0
3327056016612288512	GALAXY	STARFORMING	0.07140229	1.3711498e-05	0.6119038	0
3327056566368102400	GALAXY	--	0.061552946	1.5951675e-05	1.0402132	0
3327056841246009344	QSO	--	1.1373804	0.0006364495	0.06628287	0
3327057116123916288	STAR	F9	-0.00017936311	1.3192781e-05	0.8338379	0
3327057391001823232	STAR	A0	-0.00055759837	3.88684e-05	0.19958907	0
3327057665879730176	STAR	M3	9.9042336e-05	7.070536e-05	0.092519104	0
3327057940757637120	GALAXY	--	0.41268384	1.4011068	0.0028770566	5
3327058215635544064	GALAXY	STARFORMING	0.060996726	8.68608e-06	1.6069216	0
3327058490513451008	QSO	BROADLINE	0.82612693	0.00022074989	0.276842	0

Target Catalog

This query extracts photometric targeting information from the photoplate table. The data obtained needs to be reformatted to match the "plugmap" metadata that is stored with SDSS spectrum files, including spPlate files.

q = """SELECT s.specobjid, s.targetobjid, s.ra, s.dec,
    s.primtarget, s.sectarget,
    s.boss_target1,
    s.ancillary_target1, s.ancillary_target2,
    s.eboss_target0, s.eboss_target1, s.eboss_target2,
    p.u, p.g, p.r, p.i, p.z
FROM sdss_dr16.specobj AS s
LEFT JOIN sdss_dr16.photoplate AS p
ON s.bestobjid = p.objid
WHERE specobjid IN ({0})
ORDER BY specobjid""".format(','.join(map(str, sdss_ids.tolist())))
sdss_plugmap = qc.query(sql=q, fmt='table')
for c in ('ra', 'dec', 'primtarget', 'sectarget',
          'boss_target1',
          'ancillary_target1', 'ancillary_target2',
          'eboss_target0', 'eboss_target1', 'eboss_target2'):
    sdss_plugmap.rename_column(c, c.upper())
sdss_plugmap['OBJID'] = np.vstack((np.bitwise_and(sdss_plugmap['targetobjid'] >> 32, 2**16 - 1).data,
                                   np.bitwise_and(sdss_plugmap['targetobjid'] >> 48, 2**11 - 1).data,
                                   np.bitwise_and(sdss_plugmap['targetobjid'] >> 29, 2**3 - 1).data,
                                   np.bitwise_and(sdss_plugmap['targetobjid'] >> 16, 2**12 - 1).data,
                                   np.bitwise_and(sdss_plugmap['targetobjid'], 2**16 - 1).data)).T
sdss_plugmap['MAG'] = np.vstack((np.where(np.isnan(sdss_plugmap['u'].data), 0, sdss_plugmap['u'].data),
                                 np.where(np.isnan(sdss_plugmap['g'].data), 0, sdss_plugmap['g'].data),
                                 np.where(np.isnan(sdss_plugmap['r'].data), 0, sdss_plugmap['r'].data),
                                 np.where(np.isnan(sdss_plugmap['i'].data), 0, sdss_plugmap['i'].data),
                                 np.where(np.isnan(sdss_plugmap['z'].data), 0, sdss_plugmap['z'].data))).T

sdss.meta['plugmap'] = sdss_plugmap
sdss_plugmap

Table length=50

specobjid	targetobjid	RA	DEC	PRIMTARGET	SECTARGET	BOSS_TARGET1	ANCILLARY_TARGET1	ANCILLARY_TARGET2	EBOSS_TARGET0	EBOSS_TARGET1	EBOSS_TARGET2	u	g	r	i	z	OBJID [5]	MAG [5]
int64	int64	float64	float64	int64	int64	int64	int64	int64	int64	int64	int64	float64	float64	float64	float64	float64	int64	float64
3327035125891360768	11268994537292320	236.58816	0.91476191	96	0	0	0	0	0	0	0	21.303427	19.10137	17.512045	16.937172	16.563414	2327 .. 544	21.303427 .. 16.563414
3327036500280895488	11268994537161253	236.31498	0.83142165	64	0	0	0	0	0	0	0	20.57446	19.067663	17.930079	17.399467	16.985687	2327 .. 549	20.57446 .. 16.985687
3327037050036709376	11268994537095484	236.17657	0.91950364	64	0	0	0	0	0	0	0	19.861134	18.504723	17.763912	17.445564	17.245972	2327 .. 316	19.861134 .. 17.245972
3327038424426244096	11268994537292314	236.57992	0.84114607	32	0	0	0	0	0	0	0	21.99351	19.606033	18.029598	17.456076	17.053299	2327 .. 538	21.99351 .. 17.053299
3327038974182057984	11268994537292219	236.63067	0.85355411	64	0	0	0	0	0	0	0	19.648071	18.084349	17.2542	16.836885	16.52554	2327 .. 443	19.648071 .. 16.52554
3327040073693685760	11265262790246829	236.57247	1.3802039	64	0	0	0	0	0	0	0	20.00688	18.037868	17.137938	16.725918	16.394184	1458 .. 429	20.00688 .. 16.394184
3327040348571592704	11265262790312240	236.66305	1.4061234	96	0	0	0	0	0	0	0	20.11582	17.984798	16.497595	15.93136	15.503163	1458 .. 304	20.11582 .. 15.503163
3327040623449499648	11265262252261744	234.00554	1.1758617	64	0	0	0	0	0	0	0	--	--	--	--	--	1458 .. 368	0.0 .. 0.0
3327040898327406592	11268995074162967	236.57875	1.3024246	64	0	0	0	0	0	0	0	21.231564	18.893684	17.517618	16.979557	16.593437	2327 .. 279	21.231564 .. 16.593437
3327041173205313536	11265262790246975	236.60779	1.4217924	1	0	0	0	0	0	0	0	24.281784	21.341381	20.427032	20.001045	20.201952	1458 .. 575	24.281784 .. 20.201952
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3327055466856474624	11265262790181186	236.35198	1.4705854	64	0	0	0	0	0	0	0	20.896185	18.988594	17.842579	17.310987	16.932745	1458 .. 322	20.896185 .. 16.932745
3327056016612288512	11265262790181282	236.43226	1.3820505	64	0	0	0	0	0	0	0	19.015394	17.45155	16.701193	16.29317	15.999505	1458 .. 418	19.015394 .. 15.999505
3327056566368102400	11268994536767942	235.44215	0.87831515	64	0	0	0	0	0	0	0	20.036299	18.109346	17.067467	16.503475	16.056887	2327 .. 454	20.036299 .. 16.056887
3327056841246009344	11268994536702418	235.30366	0.84224156	1048578	0	0	0	0	0	0	0	20.499498	20.08053	19.363874	18.998253	18.802431	2327 .. 466	20.499498 .. 18.802431
3327057116123916288	284707516645555	235.31638	0.59804179	0	2	0	0	0	0	0	0	19.503473	18.193771	17.647467	17.41578	17.298328	752 .. 179	19.503473 .. 17.298328
3327057391001823232	284707516645557	235.31813	0.43006452	0	2	0	0	0	0	0	0	19.230766	18.273815	17.969347	17.86463	17.81168	752 .. 181	19.230766 .. 17.81168
3327057665879730176	11268994536768101	235.41088	0.90478873	536877056	0	0	0	0	0	0	0	23.794682	22.39897	21.048717	20.003584	19.45056	2327 .. 613	23.794682 .. 19.45056
3327057940757637120	11268994536703488	235.24978	0.85021657	0	16	0	0	0	0	0	0	--	--	--	--	--	2327 .. 1536	0.0 .. 0.0
3327058215635544064	11268994536767935	235.43123	0.86691538	64	0	0	0	0	0	0	0	19.057314	17.920666	17.439993	17.0802	16.939682	2327 .. 447	19.057314 .. 16.939682
3327058490513451008	11268994536767618	235.36244	0.8715034	1048578	0	0	0	0	0	0	0	19.41755	19.03087	18.888653	18.939404	18.742466	2327 .. 130	19.41755 .. 18.742466

Launch the Visualization

Once all the spectral data are assembled, the visualization is launched with a single plotspectra() function in the cell below. The primary input is the Spectrum1D object created above. Prospect expects a certain scaling on the input flux, so sdss.new_flux_unit() handles that (this changes the numerical scale factor on the flux unit, not the flux unit itself). Other inputs:

• zcatalog: the redshift table created above.
• model: the model spectrum used by the SDSS pipeline to obtain the redshift and classification.
• notebook: instructs Prospect to use notebook display mode (as opposed to standalone HTML).
• title, model_from_zcat, etc.: these options control certain display options that are not directly related to the input data or that need to be switched off for SDSS data. A full description is available.

Once the cell is run, it will take several seconds for the data to load and render. Then you will be able to pan and zoom on the primary flux versus wavelength display, page through different spectra, show known emission or absorption lines, etc. More details are below the display.

plotspectra(sdss.new_flux_unit(u.Unit('1e-17 erg/(cm**2 s Angstrom)')),
            zcatalog=sdss_z,
            model=(sdss.spectral_axis.value, sdss.meta['model'].to(u.Unit('1e-17 erg/(cm**2 s Angstrom)'))),
            notebook=True, title=os.path.basename('SDSS Plate %d' % plate), model_from_zcat=False, with_coaddcam=False, mask_type='PRIMTARGET', with_thumb_tab=False, with_vi_widgets=False)

Loading BokehJS ...

Visualization Interface Details

• The primary visualization area consists of three plots:
  • The flux versus wavelength in the large area on the left.
    • Pan and zoom functions can be adjusted by clicking on the icons on the upper right.
    • Display of values can be toggled by clicking on the names in the legend in the upper right. For example, to hide the noise spectrum, click on 'noise' in the legend.
  • The zoom display on the lower right. This plot is automatically updated when the mouse is hovered or moved over the large spectrum display on the left.
  • The targeting image on the upper right. This shows the image corresponding to the object. The images are provided by the Legacy Survey, and are actually deeper than SDSS images. Click on the image to take you to the Legacy Survey Viewer.
• Target information table: this table displays information related to the photometric object associated with the spectrum.
  • TARGETID: For SDSS data, this tuple of numbers corresponds to the photometric imaging run, photometric processing version, camera column ("camcol"), imaging field (within the run), and object ID within the field.
  • Target class: This contains information about why the photometric object was selected for spectroscopic observation. For SDSS data this is currently a dummy value, but will change in future versions of Prospect.
  • mag_u, ...: These are the photometric magnitudes in SDSS u, g, r, i, z filters.
• Reset X-Y range button: reset the display to the default pan and zoom values.
• OII-zoom button / Undo: pan and zoom the display to the position of the [OII] emission line. This is not necessarily relevant for SDSS data. "Undo" restores the previous pan/zoom values.
• Spectrum slider (< / >): Use these buttons or the slider to page through the loaded spectra.
• Pipeline fit (table): Displayes the classification and redshift obtained by the SDSS processing pipeline.
  • SPECTYPE: The type of object, typically 'GALAXY', 'QSO', 'STAR'.
  • SUBTYPE: A more specific classification of SPECTYPE, for example a 'STAR' may be 'K5' or a 'GALAXY' may be 'STARFORMING'.
  • Z: The redshift.
  • ZERR: The uncertainty on Z.
  • ZWARN: If the processing pipeline produced errors, this value will be non-zero.
  • $\Delta\chi^2$: the goodness of fit of the fitted model relative to the second-best model.
• Redshift box (orange): Use this box to adjust the redshift of the model on the display.
  • Rough and fine tuning adjust the displayed redshift in units of hundredths and ten-thousandths, respectively.
  • Reset to z_pipe: restore the displayed redshift to the redshift value obtained from the SDSS pipeline.
  • Copy z to VI: this button is disabled.
• Gaussian Sigma: SDSS spectra are inherently faint and noisy. Smoothing will reduce the "jaggedness" caused by noise. This is especially useful when comparing to the fitted model or investigating spectra lines.
• Emission lines / Absorption lines: Toggle the display of known spectral lines. If "Show only major lines" is checked, only the spectral lines most likely to be visible are displayed.
• Obs / Rest: Display flux as a function of observed wavelength or rest wavelength, respectively.

SDSS Spectra and Redshifts from Files

For reference, and to compare timing, below is how one would fetch spectra and redshift metadata from file-based storage. First, obtain the path to the files in the Astro Data Lab file service.

spectro_redux = 'sdss_dr16://' + os.path.join('sdss', 'spectro', 'redux')
run2d = '26'
plate = 2955
mjd = '54562'
sdss_spectra = os.path.join(spectro_redux, run2d, str(plate), f'spPlate-{plate:d}-{mjd}.fits')
sdss_redshifts = os.path.join(spectro_redux, run2d, str(plate), f'spZbest-{plate:d}-{mjd}.fits')
print(sdss_spectra)
print(sdss_redshifts)

sdss_dr16://sdss/spectro/redux/26/2955/spPlate-2955-54562.fits
sdss_dr16://sdss/spectro/redux/26/2955/spZbest-2955-54562.fits

Next, read the spPlate file. This automatically loads the targeting metadata.

%%time
sdss_file = read_spPlate(sc.get(sdss_spectra, mode='fileobj'), limit=50)
sdss_file

CPU times: user 349 ms, sys: 260 ms, total: 609 ms
Wall time: 2.22 s

<Spectrum1D(flux=<Quantity [[11.0880165 , 11.084556  , 11.081101  , ..., 11.787075  ,
            11.787052  , 11.787028  ],
           [ 4.371129  ,  4.36966   ,  4.3681927 , ...,  2.2523484 ,
             2.2523346 ,  2.2523208 ],
           [ 0.85558206,  0.8553027 ,  0.8550237 , ...,  8.030891  ,
             8.030855  ,  8.03082   ],
           ...,
           [12.411868  , 12.408562  , 12.405259  , ..., 25.452364  ,
            25.452513  , 25.45266   ],
           [-1.97795   , -1.9774021 , -1.976855  , ..., 15.674774  ,
            15.674834  , 15.674895  ],
           [ 0.6398605 ,  0.63967246,  0.63948476, ..., -0.11592854,
            -0.11592867, -0.1159288 ]] 1e-17 erg / (Angstrom cm2 s)>, spectral_axis=<SpectralAxis [3802.76948244, 3803.64520329, 3804.5211258 , ..., 9217.22098659,
   9219.34357452, 9221.46665124] Angstrom>, uncertainty=InverseVariance([[0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.],
                 ...,
                 [0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.],
                 [0., 0., 0., ..., 0., 0., 0.]]))>

Read the spZbest file, which contains the redshift catalog.

sdss_z_file, sdss_model_file = read_spZbest(sc.get(sdss_redshifts, mode='fileobj'), limit=50)

Launch the visualization. Reading from files produces slightly different scaling on the flux values, but otherwise the options are the same as the example above.

plotspectra(sdss_file,
            zcatalog=sdss_z_file,
            model=(sdss_model_file.spectral_axis.value, sdss_model_file.flux.value,),
            notebook=True, title=os.path.basename('SDSS Plate %d' % plate), model_from_zcat=False, with_coaddcam=False, mask_type='PRIMTARGET', with_thumb_tab=False, with_vi_widgets=False)

Loading BokehJS ...

References & Resources

• Getting Started with Spectral Data

end_time = time.time()
print('Notebook run time: %.3g sec' % (end_time - start_time))

Notebook run time: 37.2 sec